VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares

Ling Shen; Fuxing Liu; Li Huang; Guangyi Liu; Liqian Zhou; Lihong Peng

doi:10.1016/j.compbiomed.2021.105119

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares

Comput Biol Med. 2022 Jan:140:105119. doi: 10.1016/j.compbiomed.2021.105119. Epub 2021 Dec 7.

Authors

Ling Shen¹, Fuxing Liu¹, Li Huang², Guangyi Liu¹, Liqian Zhou³, Lihong Peng⁴

Affiliations

¹ School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China.
² Academy of Arts and Design, Tsinghua University, Beijing, 10 084, Beijing, China; The Future Laboratory, Tsinghua University, Beijing, 10 084, Beijing, China.
³ School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China. Electronic address: zhoulq11@163.com.
⁴ School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China. Electronic address: plhhnu@163.com.

Abstract

Background: A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19.

Methods: In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results.

Results: In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs.

Conclusions: Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

Keywords: Antiviral drug; Laplacian regularized least squares; Molecular docking; Molecular dynamics simulation; SARS-CoV-2; Unbalanced bi-random walk; Virus-drug association.